Agent-Skills.md

Agent Skills: Discover Codebase Enhancements

Use when the user asks for a deep codebase analysis to identify and rank improvements, optimizations, architectural enhancements, or potential bugs aligned to developer, end-user, and agent jobs-to-be-done.

UncategorizedID: kasperjunge/agent-resources/discover-codebase-enhancements

Repository

kasperjunge/agent-resources
kasperjungeLicense: MIT
39730

Install this agent skill to your local

pnpm dlx add-skill https://github.com/kasperjunge/agent-resources/tree/HEAD/.opencode/skill/discover-codebase-enhancements

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.opencode/skill/discover-codebase-enhancements/SKILL.md

Skill Metadata

Name
discover-codebase-enhancements
Description
Use when the user asks for a deep codebase analysis to identify and rank improvements, optimizations, architectural enhancements, or potential bugs aligned to developer, end-user, and agent jobs-to-be-done.

Discover Codebase Enhancements

Overview

Spend significant time crawling and analyzing the codebase to surface high-impact improvements. Center findings on the jobs-to-be-done of the codebase, developers, end users, and AI agents working in the repo.

Inputs (ask if missing, max 5)

  • Target area or scope (whole repo or specific modules)
  • Primary user jobs-to-be-done and business goals
  • Known pain points or incidents
  • Constraints (time, risk tolerance, release window)
  • Evidence sources allowed (tests, metrics, logs)

Jobs-to-Be-Done Lens

  • Codebase: reliability, simplicity, maintainability
  • Developers: speed, clarity, safe changes
  • End users: correctness, performance, usability
  • AI agents: discoverability, consistency, explicit patterns

Workflow

  1. Deep crawl
    • Read architecture docs, READMEs, key modules, and tests.
    • Search for hotspots (TODO/FIXME, large files, duplication, complex flows).
  2. Evidence gathering
    • Note error-prone areas, missing tests, performance risks, and coupling.
    • Capture references to files/functions and concrete symptoms.
  3. Opportunity synthesis
    • Group findings by theme: correctness, performance, DX, architecture, tests, tooling.
  4. Impact scoring
    • Rate impact, effort, risk, and evidence strength.
  5. Ranked recommendations
    • Present top enhancements with rationale and expected outcomes.

Output Format

## Codebase Enhancement Discovery

### Context Summary
[1-3 sentences]

### JTBD Summary
- Codebase: ...
- Developers: ...
- End users: ...
- AI agents: ...

### Evidence Sources
- Files/modules reviewed: ...
- Patterns searched: ...
- Tests or metrics considered: ...

### Ranked Enhancements
1) [Enhancement]
   - Category: ...
   - Impact: high | Effort: medium | Risk: low | Evidence: moderate
   - Rationale: ...
   - Affected areas: ...

### Quick Wins
- ...

### Open Questions
- ...

Quick Reference

  • Spend more time exploring than feels necessary.
  • Prefer evidence-backed findings over speculation.
  • Center recommendations on user and developer outcomes.

Common Mistakes

  • Skimming without enough code context
  • Listing fixes without evidence or impact scoring
  • Ignoring AI agent or developer workflows
  • Recommending changes that fight existing architecture